Kriging metamodel with modified nugget-effect: The heteroscedastic variance case

Authors:
J. Yin;S. H. Ng;K. M. Ng
Affiliations:
Department of Industrial and System Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore;Department of Industrial and System Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore;Department of Industrial and System Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore
Venue:
Computers and Industrial Engineering
Year:
2011

Citing 6
Cited 3

Statistical tools for simulation practitioners

Statistical tools for simulation practitioners
Improved Design of Queueing Simulation Experiments with Highly Heteroscedastic Responses

Operations Research
Global Optimization of Stochastic Black-Box Systems via Sequential Kriging Meta-Models

Journal of Global Optimization
A metamodel optimization methodology based on multi-level fuzzy clustering space reduction strategy and its applications

Computers and Industrial Engineering
Stochastic Kriging for Simulation Metamodeling

Operations Research
A systematic comparison of metamodeling techniques for simulation optimization in Decision Support Systems

Applied Soft Computing

Bayesian Kriging Analysis and Design for Stochastic Simulations

ACM Transactions on Modeling and Computer Simulation (TOMACS)
Convex and monotonic bootstrapped kriging

Proceedings of the Winter Simulation Conference
A benchmark of kriging-based infill criteria for noisy optimization

Structural and Multidisciplinary Optimization

Quantified Score

Hi-index	0.00

Visualization

Abstract

Metamodels are commonly used to approximate and analyze simulation models. However, in cases where the simulation output variances are non-zero and not constant, many of the current metamodels which assume homogeneity, fail to provide satisfactory estimation. In this paper, we present a kriging model with modified nugget-effect adapted for simulations with heterogeneous variances. The new model improves the estimations of the sensitivity parameters by explicitly accounting for location dependent non-constant variances and smoothes the kriging predictor's output accordingly. We look into the effects of stochastic noise on the parameter estimation for the classic kriging model that assumes deterministic outputs and note that the stochastic noise increases the variability of the classic parameter estimation. The nugget-effect and proposed modified nugget-effect stabilize the estimated parameters and decrease the erratic behavior of the predictor by penalizing the likelihood function affected by stochastic noise. Several numerical examples suggest that the kriging model with modified nugget-effect outperforms the kriging model with nugget-effect and the classic kriging model in heteroscedastic cases.